U.S. patent application number 15/854028 was filed with the patent office on 2019-02-07 for speaker recognition based on vibration signals.
This patent application is currently assigned to Intel Corporation. The applicant listed for this patent is Intel Corporation. Invention is credited to Hector Cordourier Maruri, Jonathan Huang.
Application Number | 20190043512 15/854028 |
Document ID | / |
Family ID | 65230535 |
Filed Date | 2019-02-07 |
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United States Patent
Application |
20190043512 |
Kind Code |
A1 |
Huang; Jonathan ; et
al. |
February 7, 2019 |
SPEAKER RECOGNITION BASED ON VIBRATION SIGNALS
Abstract
An embodiment of a semiconductor package apparatus may include
technology to acquire vibration information corresponding to a
speaker, and identify the speaker based on the vibration
information. Other embodiments are disclosed and claimed.
Inventors: |
Huang; Jonathan;
(Pleasanton, CA) ; Cordourier Maruri; Hector;
(Guadalajara, MX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Intel Corporation |
Santa Clara |
CA |
US |
|
|
Assignee: |
Intel Corporation
Santa Clara
CA
|
Family ID: |
65230535 |
Appl. No.: |
15/854028 |
Filed: |
December 26, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G10L 17/26 20130101;
G10L 17/22 20130101; G10L 17/12 20130101; G10L 17/06 20130101; G10L
25/84 20130101 |
International
Class: |
G10L 17/22 20060101
G10L017/22; G10L 25/84 20060101 G10L025/84; G10L 17/26 20060101
G10L017/26; G10L 17/06 20060101 G10L017/06 |
Claims
1. An electronic processing system, comprising: a processor; memory
communicatively coupled to the processor; and logic communicatively
coupled to the processor to: acquire vibration information
corresponding to a speaker, and identify the speaker based on the
vibration information.
2. The system of claim 1, wherein the logic is further to: acquire
audio information corresponding to the speaker; and identify the
speaker based on both the audio information and the vibration
information.
3. The system of claim 2, wherein the logic is further to: perform
speaker recognition based on the audio information to determine a
first recognition score; perform speaker recognition based on the
vibration information to determine a second recognition score; and
identify the speaker based on an average of the first recognition
score and the second recognition score.
4. The system of claim 3, wherein the logic is further to:
determine a level of background noise; compare the level of
background noise against a threshold; identify the speaker based on
both the audio information and the vibration information if the
level of background noise is less than the threshold; and identify
the speaker based on only the vibration information if the level of
background noise is greater than or equal to the threshold.
5. The system of claim 3, wherein the logic is further to:
determine a level of background noise; apply weights to the first
and second recognition scores based on the level of background
noise; and identify the speaker based on an average of the weighted
first recognition score and the weighted second recognition
score.
6. The system of claim 2, wherein the logic is further to: acquire
the audio information from a microphone; and acquire vibration
information from an accelerometer.
7. A semiconductor package apparatus, comprising: one or more
substrates; and logic coupled to the one or more substrates,
wherein the logic is at least partly implemented in one or more of
configurable logic and fixed-functionality hardware logic, the
logic coupled to the one or more substrates to: acquire vibration
information corresponding to a speaker, and identify the speaker
based on the vibration information.
8. The apparatus of claim 7, wherein the logic is further to:
acquire audio information corresponding to the speaker; and
identify the speaker based on both the audio information and the
vibration information.
9. The apparatus of claim 8, wherein the logic is further to:
perform speaker recognition based on the audio information to
determine a first recognition score; perform speaker recognition
based on the vibration information to determine a second
recognition score; and identify the speaker based on an average of
the first recognition score and the second recognition score.
10. The apparatus of claim 9, wherein the logic is further to:
determine a level of background noise; compare the level of
background noise against a threshold; identify the speaker based on
both the audio information and the vibration information if the
level of background noise is less than the threshold; and identify
the speaker based on only the vibration information if the level of
background noise is greater than or equal to the threshold.
11. The apparatus of claim 9, wherein the logic is further to:
determine a level of background noise; apply weights to the first
and second recognition scores based on the level of background
noise; and identify the speaker based on an average of the weighted
first recognition score and the weighted second recognition
score.
12. The apparatus of claim 8, wherein the logic is further to:
acquire the audio information from a microphone; and acquire
vibration information from an accelerometer.
13. The semiconductor package apparatus of claim 7, wherein the
logic coupled to the one or more substrates includes transistor
channel regions that are positioned within the one or more
substrates.
14. A method of identifying a speaker, comprising: acquiring
vibration information corresponding to a speaker; and identifying
the speaker based on the vibration information.
15. The method of claim 14, further comprising: acquiring audio
information corresponding to the speaker; identifying the speaker
based on both the audio information and the vibration
information.
16. The method of claim 15, further comprising: performing speaker
recognition based on the audio information to determine a first
recognition score; performing speaker recognition based on the
vibration information to determine a second recognition score; and
identifying the speaker based on an average of the first
recognition score and the second recognition score.
17. The method of claim 16, further comprising: determining a level
of background noise; comparing the level of background noise
against a threshold; identifying the speaker based on both the
audio information and the vibration information if the level of
background noise is less than the threshold; and identifying the
speaker based on only the vibration information if the level of
background noise is greater than or equal to the threshold.
18. The method of claim 16, further comprising: determining a level
of background noise; applying weights to the first and second
recognition scores based on the level of background noise; and
identifying the speaker based on an average of the weighted first
recognition score and the weighted second recognition score.
19. The method of claim 15, further comprising: acquiring the audio
information from a microphone; and acquiring vibration information
from an accelerometer.
20. At least one computer readable medium, comprising a set of
instructions, which when executed by a computing device, cause the
computing device to: acquire vibration information corresponding to
a speaker; and identify the speaker based on the vibration
information.
21. The at least one computer readable medium of claim 20,
comprising a further set of instructions, which when executed by
the computing device, cause the computing device to: acquire audio
information corresponding to the speaker; identify the speaker
based on both the audio information and the vibration
information.
22. The at least one computer readable medium of claim 21,
comprising a further set of instructions, which when executed by
the computing device, cause the computing device to: perform
speaker recognition based on the audio information to determine a
first recognition score; perform speaker recognition based on the
vibration information to determine a second recognition score; and
identify the speaker based on an average of the first recognition
score and the second recognition score.
23. The at least one computer readable medium of claim 22,
comprising a further set of instructions, which when executed by
the computing device, cause the computing device to: determine a
level of background noise; compare the level of background noise
against a threshold; identify the speaker based on both the audio
information and the vibration information if the level of
background noise is less than the threshold; and identify the
speaker based on only the vibration information if the level of
background noise is greater than or equal to the threshold.
24. The at least one computer readable medium of claim 22,
comprising a further set of instructions, which when executed by
the computing device, cause the computing device to: determine a
level of background noise; apply weights to the first and second
recognition scores based on the level of background noise; and
identify the speaker based on an average of the weighted first
recognition score and the weighted second recognition score.
25. The at least one computer readable medium of claim 21,
comprising a further set of instructions, which when executed by
the computing device, cause the computing device to: acquire the
audio information from a microphone; and acquire vibration
information from an accelerometer.
Description
TECHNICAL FIELD
[0001] Embodiments generally relate to speaker recognition systems.
More particularly, embodiments relate to speaker recognition based
on vibration signals.
BACKGROUND
[0002] A system may include technology to identify a user of the
system. On some computer devices, a password or personal
identification number (PIN) may be entered by a keyboard (e.g.,
physical or virtual). Some devices may utilize biometric features
(e.g., fingerprints, retinal images, etc.) to identify the user.
Speech recognition may refer to the recognition of spoken words by
a computing device, while voice recognition or speaker recognition
may refer to identifying the speaker of the spoken words as opposed
to what the speaker said.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] The various advantages of the embodiments will become
apparent to one skilled in the art by reading the following
specification and appended claims, and by referencing the following
drawings, in which:
[0004] FIG. 1 is a block diagram of an example of an electronic
processing system according to an embodiment;
[0005] FIG. 2 is a block diagram of an example of a semiconductor
package apparatus according to an embodiment;
[0006] FIGS. 3A to 3D are flowcharts of an example of a method of
identifying a speaker according to an embodiment;
[0007] FIG. 4 is a block diagram of an example of wearable
apparatus according to an embodiment;
[0008] FIG. 5 is an illustrative diagram of a speaker wearing a
wearable device according to an embodiment;
[0009] FIGS. 6A to 6B are process flow diagrams of an example of
vibration-based speaker recognition according to an embodiment;
[0010] FIG. 7 is a process flow diagram of another example of
vibration-based speaker recognition according to an embodiment;
[0011] FIG. 8 is a process flow diagram of another example of
vibration-based speaker recognition according to an embodiment;
[0012] FIG. 9 is a block diagram of an example of a system having a
navigation controller according to an embodiment; and
[0013] FIG. 10 is a block diagram of an example of a system having
a small form factor according to an embodiment.
DESCRIPTION OF EMBODIMENTS
[0014] Turning now to FIG. 1, an embodiment of an electronic
processing system 10 may include a processor 11, memory 12
communicatively coupled to the processor 11, and logic 13
communicatively coupled to the processor 11 to acquire vibration
information corresponding to a speaker, and identify the speaker
based on the vibration information. In some embodiments, the logic
13 may be further configured to acquire audio information
corresponding to the speaker, and identify the speaker based on
both the audio information and the vibration information. For
example, the logic 13 may be configured to perform speaker
recognition based on the audio information to determine a first
recognition score, perform speaker recognition based on the
vibration information to determine a second recognition score, and
identify the speaker based on an average of the first recognition
score and the second recognition score. Some embodiments of the
system 10 may utilize a background noise threshold. For example,
the logic 13 may be further configured to determine a level of
background noise, compare the level of background noise against a
threshold, identify the speaker based on both the audio information
and the vibration information if the level of background noise is
less than the threshold, and identify the speaker based on only the
vibration information if the level of background noise is greater
than or equal to the threshold. Some embodiments of the system 10
may utilize a weighted average based on the level of background
noise. For example, the logic 13 may alternatively, or
additionally, be configured to determine a level of background
noise, apply weights to the first and second recognition scores
based on the level of background noise, and identify the speaker
based on an average of the weighted first recognition score and the
weighted second recognition score. In some embodiments, the logic
13 may be configured to acquire the audio information from a
microphone, and/or to acquire vibration information from an
accelerometer. For example, the vibration information may
correspond to one or more of a nasal vibration, a facial vibration,
a forehead vibration, a temple vibration, a throat vibration and a
neck vibration.
[0015] Embodiments of each of the above processor 11, memory 12,
logic 13, and other system components may be implemented in
hardware, software, or any suitable combination thereof. For
example, hardware implementations may include configurable logic
such as, for example, programmable logic arrays (PLAs), field
programmable gate arrays (FPGAs), complex programmable logic
devices (CPLDs), or fixed-functionality logic hardware using
circuit technology such as, for example, application specific
integrated circuit (ASIC), complementary metal oxide semiconductor
(CMOS) or transistor-transistor logic (TTL) technology, or any
combination thereof.
[0016] Alternatively, or additionally, all or portions of these
components may be implemented in one or more modules as a set of
logic instructions stored in a machine- or computer-readable
storage medium such as random access memory (RAM), read only memory
(ROM), programmable ROM (PROM), firmware, flash memory, etc., to be
executed by a processor or computing device. For example, computer
program code to carry out the operations of the components may be
written in any combination of one or more operating system (OS)
applicable/appropriate programming languages, including an
object-oriented programming language such as PYTHON, PERL, JAVA,
SMALLTALK, C++, C# or the like and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. For example, the memory 12,
persistent storage media, or other system memory may store a set of
instructions which when executed by the processor 11 cause the
system 10 to implement one or more components, features, or aspects
of the system 10 (e.g., the logic 13, acquiring audio information
corresponding to a speaker, acquiring vibration information
corresponding to the speaker, identifying the speaker based on both
the audio information and the vibration information, etc.).
[0017] Turning now to FIG. 2, an embodiment of a semiconductor
package apparatus 20 may include one or more substrates 21, and
logic 22 coupled to the one or more substrates 21, wherein the
logic 22 is at least partly implemented in one or more of
configurable logic and fixed-functionality hardware logic. The
logic 22 coupled to the one or more substrates may be configured to
acquire vibration information corresponding to a speaker, and
identify the speaker based on the vibration information. In some
embodiments, the logic 22 may be further configured to acquire
audio information corresponding to the speaker, and identify the
speaker based on both the audio information and the vibration
information. For example, the logic 22 may be configured to perform
speaker recognition based on the audio information to determine a
first recognition score, perform speaker recognition based on the
vibration information to determine a second recognition score, and
identify the speaker based on an average of the first recognition
score and the second recognition score. Some embodiments of the
apparatus 20 may utilize a background noise threshold. For example,
the logic 22 may be further configured to determine a level of
background noise, compare the level of background noise against a
threshold, identify the speaker based on both the audio information
and the vibration information if the level of background noise is
less than the threshold, and identify the speaker based on only the
vibration information if the level of background noise is greater
than or equal to the threshold. Some embodiments of the apparatus
20 may utilize a weighted average based on the level of background
noise. For example, the logic 22 may alternatively, or
additionally, be configured to determine a level of background
noise, apply weights to the first and second recognition scores
based on the level of background noise, and identify the speaker
based on an average of the weighted first recognition score and the
weighted second recognition score. In some embodiments, the logic
22 may be configured to acquire the audio information from a
microphone, and/or to acquire vibration information from an
accelerometer. For example, the vibration information may
correspond to one or more of a nasal vibration, a facial vibration,
a forehead vibration, a temple vibration, a throat vibration and a
neck vibration. In some embodiments, the logic 22 coupled to the
one or more substrates 21 may include transistor channel regions
that are positioned within the one or more substrates.
[0018] Embodiments of logic 22, and other components of the
apparatus 20, may be implemented in hardware, software, or any
combination thereof including at least a partial implementation in
hardware. For example, hardware implementations may include
configurable logic such as, for example, PLAs, FPGAs, CPLDs, or
fixed-functionality logic hardware using circuit technology such
as, for example, ASIC, CMOS, or TTL technology, or any combination
thereof. Additionally, portions of these components may be
implemented in one or more modules as a set of logic instructions
stored in a machine- or computer-readable storage medium such as
RAM, ROM, PROM, firmware, flash memory, etc., to be executed by a
processor or computing device. For example, computer program code
to carry out the operations of the components may be written in any
combination of one or more OS applicable/appropriate programming
languages, including an object-oriented programming language such
as PYTHON, PERL, JAVA, SMALLTALK, C++, C# or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages.
[0019] The apparatus 20 may implement one or more aspects of the
method 30 (FIGS. 3A to 3D), the process flow 59 (FIGS. 6A to 6B),
the process flow 70 (FIG. 7), and/or the process flow 80 (FIG. 9),
discussed below. The illustrated apparatus 20 includes one or more
substrates 21 (e.g., silicon, sapphire, gallium arsenide) and logic
22 (e.g., transistor array and other integrated circuit/IC
components) coupled to the substrate(s) 21. The logic 22 may be
implemented at least partly in configurable logic or
fixed-functionality logic hardware. In one example, the logic 22
includes transistor channel regions that are positioned (e.g.,
embedded) within the substrate(s) 21. Thus, the interface between
the logic 22 and the substrate(s) 21 may not be an abrupt junction.
The logic 22 may also be considered to include an epitaxial layer
that is grown on an initial wafer of the substrate(s) 21.
[0020] Turning now to FIGS. 3A to 3D, an embodiment of a method 30
of identifying a speaker may include acquiring vibration
information corresponding to the speaker at block 31, and
identifying the speaker based on the vibration information at block
32. Some embodiments of the method 30 may further include acquiring
audio information corresponding to the speaker at block 33, and
identifying the speaker based on both the audio information and the
vibration information at block 34. For example, the method 30 may
also include performing speaker recognition based on the audio
information to determine a first recognition score at block 35,
performing speaker recognition based on the vibration information
to determine a second recognition score at block 36, and
identifying the speaker based on an average of the first
recognition score and the second recognition score at block 37.
Some embodiments of the method may further include determining a
level of background noise at block 38, comparing the level of
background noise against a threshold at block 39, identifying the
speaker based on both the audio information and the vibration
information if the level of background noise is less than the
threshold at block 40, and identifying the speaker based on only
the vibration information if the level of background noise is
greater than or equal to the threshold at block 41.
[0021] Alternatively, or additionally, some embodiments of the
method 30 may include determining a level of background noise at
block 42, applying weighting to the first and second recognition
scores based on the level of background noise at block 43, and
identifying the speaker based on an average of the weighted first
recognition score and the weighted second recognition score at
block 44. For example, the method 30 may include acquiring the
audio information from a microphone at block 45, and acquiring
vibration information from an accelerometer at block 46. In any of
the embodiments herein, the vibration information may correspond to
one or more of a nasal vibration, a facial vibration, a forehead
vibration, a temple vibration, a throat vibration and a neck
vibration at block 47.
[0022] Embodiments of the method 30 may be implemented in a system,
apparatus, computer, device, etc., for example, such as those
described herein. More particularly, hardware implementations of
the method 30 may include configurable logic such as, for example,
PLAs, FPGAs, CPLDs, or in fixed-functionality logic hardware using
circuit technology such as, for example, ASIC, CMOS, or TTL
technology, or any combination thereof. Alternatively, or
additionally, the method 30 may be implemented in one or more
modules as a set of logic instructions stored in a machine- or
computer-readable storage medium such as RAM, ROM, PROM, firmware,
flash memory, etc., to be executed by a processor or computing
device. For example, computer program code to carry out the
operations of the components may be written in any combination of
one or more OS applicable/appropriate programming languages,
including an object-oriented programming language such as PYTHON,
PERL, JAVA, SMALLTALK, C++, C# or the like and conventional
procedural programming languages, such as the "C" programming
language or similar programming languages.
[0023] For example, the method 30 may be implemented on a computer
readable medium as described in connection with Examples 20 to 25
below. Embodiments or portions of the method 30 may be implemented
in firmware, applications (e.g., through an application programming
interface (API)), or driver software running on an operating system
(OS).
[0024] Turning now to FIG. 4, an embodiment of a wearable apparatus
50 may include a microphone 51, an accelerometer 52, and a speaker
recognition module 53. For example, the wearable apparatus 50 may
have any suitable form factor such as a headset, a headband, a hat,
a cap, a headset, eyeglasses, etc. The microphone 51 may be
positioned on the wearable apparatus to capture audible speech
sounds from a speaker wearing the wearable apparatus 50. Audio
information from the microphone may include digital representations
of the audible speech sounds. The accelerometer 52 may be
positioned on the wearable apparatus to be proximate to or against
the skin of the speaker to capture vibrations from bone and/or
tissue conduction simultaneously with the microphone. Vibration
information from the accelerometer may include digital
representations of non-audible vibrations corresponding to the
speaker when the speaker produces the audible speech sounds. For
example, the vibration information may correspond to one or more of
a nasal vibration, a facial vibration, a forehead vibration, a
temple vibration, a throat vibration and a neck vibration. The
speaker recognition module 53 may include technology to acquire the
audio information corresponding to the speaker from the microphone
51, to acquire the vibration information corresponding to the
speaker from the accelerometer 52, and to identify the speaker
based on both the audio information and the vibration
information.
[0025] In some embodiments, the speaker recognition module 53 may
be configured to perform speaker recognition based on the audio
information to determine a first recognition score, perform speaker
recognition based on the vibration information to determine a
second recognition score, and identify the speaker based on an
average of the first recognition score and the second recognition
score. Some embodiments of the apparatus 50 may utilize a
background noise threshold. For example, the speaker recognition
module 53 may be further configured to determine a level of
background noise (e.g., utilizing the microphone 51), compare the
level of background noise against a threshold, identify the speaker
based on both the audio information and the vibration information
if the level of background noise is less than the threshold, and
identify the speaker based on only the vibration information if the
level of background noise is greater than or equal to the
threshold. Some embodiments of the apparatus 50 may utilize a
weighted average based on the level of background noise. For
example, the speaker recognition module 53 may alternatively, or
additionally, be configured to determine a level of background
noise, apply weights to the first and second recognition scores
based on the level of background noise, and identify the speaker
based on an average of the weighted first recognition score and the
weighted second recognition score. In some embodiments, all or
portions of the speaker recognition module 53 may be implemented on
a user device which is communicatively coupled to the wearable
apparatus 50 (e.g., wired or wirelessly), such that the microphone
and/or accelerometer related information may be acquired indirectly
(e.g., through an antenna and radio communication). For example,
the user device may include a smartphone, a tablet, a laptop
computer, a notebook computer, or another portable computing
device. In some embodiments, all or portions of the speaker
recognition module 53 may be implemented on a server which is
communicatively coupled to the wearable apparatus 50 (e.g., wired
or wirelessly). For example, the microphone and/or accelerometer
related information may be uploaded to the cloud to perform the
speaker recognition based on the microphone and/or accelerometer
information.
[0026] Embodiments of the microphone 51, the accelerometer 52, the
speaker recognition module 53, and other components of the wearable
apparatus 50, may be implemented in hardware, software, or any
combination thereof including at least a partial implementation in
hardware. For example, hardware implementations may include
configurable logic such as, for example, PLAs, FPGAs, CPLDs, or
fixed-functionality logic hardware using circuit technology such
as, for example, ASIC, CMOS, or TTL technology, or any combination
thereof. Additionally, portions of these components may be
implemented in one or more modules as a set of logic instructions
stored in a machine- or computer-readable storage medium such as
RAM, ROM, PROM, firmware, flash memory, etc., to be executed by a
processor or computing device. For example, computer program code
to carry out the operations of the components may be written in any
combination of one or more OS applicable/appropriate programming
languages, including an object-oriented programming language such
as PYTHON, PERL, JAVA, SMALLTALK, C++, C# or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages.
[0027] Some embodiments may advantageously provide speaker
recognition technology utilizing vibration sensing wearable devices
such as glasses. Some embodiments may provide improved technology
for speaker recognition (SR), also referred to as voice metrics,
which may incorporate speech signals acquired by nasal vibration
signals. Passwords and PINs may sometimes be cumbersome for users
(e.g., especially in wearable devices) and may be ineffective if
the keyword is stolen or if the interface to enter the password/PIN
is not conveniently available (e.g., for wearable devices such as
smart glasses, smart watches, a headset, etc.). Dedicated hardware
for biometric feature detection may be expensive, and may increase
the cost for small, wearable devices. Despite some progress,
audio-only based speaker recognition technology may have problems
involving recognition accuracy that may fall short of other modes
of biometric authentication, vulnerability to replay attack (e.g.,
playback of a user's speech using a good quality loudspeaker to
enter the system), and/or performance that degrades in the presence
of noise. Some embodiments may advantageously overcome one or more
of the foregoing problems with user identification with technology
to acquire simultaneous recordings of speech by a microphone and an
accelerometer, and technology to recognize the speaker using both
the microphone and the accelerometer information. For example, when
a background noise level is low, performing parallel speaker
recognition on both an audio data stream and a vibration data
stream and averaging the two recognition scores may advantageously
provide improved error rates as compared to utilizing either data
stream alone. Under noisy conditions the speech data from the
microphone may deteriorate. Some embodiments may selectively
utilize only the recognition score of the accelerometer stream
under noisy conditions. For a wearable device, some embodiments may
advantageously provide less cumbersome technology to accurately
identify and/or authenticate the person wearing the device.
[0028] Turning now to FIG. 5, a speaker 55 may be wearing an
embodiment of a wearable device 56 which includes both a microphone
57 and an accelerometer 58. The microphone 57 may be positioned on
the wearable device 56 to detect audible speech sounds from the
speaker 55, while the accelerometer 58 may be positioned on the
wearable device 56 to simultaneously detect non-audible nasal
vibrations from the speaker 55. For example, the accelerometer 58
may be positioned near the speaker's nose. In some embodiments, the
wearable device 56 may be in the form factor of a headset where the
accelerometer 58 may be supported by an extension of the headset
that goes either above the eye (e.g., as shown) or under the eye
across the cheek of the speaker 55. In other embodiments, the
wearable device 56 may have other form factors such as glasses
where the accelerometer may be positioned in the bridge or nose
piece of the glasses. In some embodiments, other facial vibrations
may additionally or alternatively be detected with one or more
additional accelerometers or other vibration sensors (e.g., a hat,
cap, or headband form factor wearable device may position an
accelerometer against the speaker's forehead). The wearable device
56 may also include a BLUETOOTH, a WIFI, and/or a cellular radio
and antenna to communicate the sensor information to another
computing device (e.g., a user device, a server, the cloud, etc.)
to perform the speaker recognition as described herein. For
example, the wearable device may communicate the sensor information
to a user device (e.g., a smartphone carried by the speaker 55) to
perform the speaker recognition as described herein, or the user
device may further communicate the sensor information to another
computing device (e.g., a server or the cloud) to perform the
speaker recognition as described herein.
[0029] Advantageously, some embodiments utilization of nasal
vibration signals acquired by accelerometer may overcome one or
more problems with speaker recognition technology which utilizes
only audio-based technology. In particular, the vibration
information combined with the audio information may provide
improved accuracy. For example, when the accelerometer signal is
combined with the microphone signal some embodiments may improve
the overall robustness over the baseline. In some embodiments, the
microphone and accelerometer signals may advantageously provide
highly complementary characteristics for the task of speaker
recognition. Some embodiments may also advantageously provide
resistance against replay attacks. For example, the vibration
sensor may detect the speech by contact with the person who is
speaking. Accordingly, replay through the air may not be able to
attack the system. Some embodiments may also advantageously improve
noise robustness. For example, speech acquired through a vibration
sensor may pick up much less ambient noise than a microphone.
[0030] Turning now to FIGS. 6A to 6B, an embodiment of a speaker
recognition process flow 59 may include providing a microphone
signal 60 to a signal-to-noise ratio (SNR) estimation module 61 in
parallel with providing an accelerometer signal 62 to an
accelerometer speaker ID classifier 63. In some embodiments, the
accelerometer signal 62 may also be provided to the SNR estimation
module 61 to help discriminate which moments of the microphone
signal 60 may correspond to the user's voice and which may
correspond just to background noise. The result of the SNR may be
compared to a threshold at block 64. In a noisy environment, the
SNR may not be lower than the threshold and a score 65
corresponding only to the accelerometer speaker ID classifier 63
may be used for the speaker ID at block 66 (e.g., see FIG. 6A). In
a low noise environment, the SNR may be lower than the threshold
and the microphone signal 60 may be provided to a microphone
speaker ID classifier 67. The scores from both the microphone
speaker ID classifier 67 and the accelerometer speaker ID
classifier 63 may be combined to provide a score fusion 68, and the
score fusion 68 may be used for the speaker ID at block 69 (see
FIG. 6B).
[0031] Any suitable speaker recognition technology may be utilized
for the microphone speaker ID classifier 67 and the accelerometer
speaker ID classifier. Non-limiting examples of suitable speaker
recognition (SR) technology include Mel Frequency Cepstral
Coefficients (MFCCs) front-end technology together with a
machine-learning classifier on the back-end. The classifier in the
back-end may include Gaussian mixture model (GMM) technology, GMM
using universal background model (GMM-UBM) technology, GMM-UBM, GMM
using support vector machine (GMM-SVM) technology,
i-vector/probabilistic linear discriminant analysis (PLDA)
technology, etc. For a short-duration task such as speaker
identification, GMM-SVM technology may be preferred.
[0032] The output of the SR may be a score indicating a likelihood
of a match to an enrolled speaker. The decision to accept or reject
the score may be determined by a threshold, which may be a tradeoff
between a false reject rate (FRR) versus a false accept rate (FAR).
If the threshold is selected such that FRR=FAR, the resulting error
rate may be referred to as the equal error rate (EER). Some
embodiments may advantageously provide a lower EER as compared to
some other systems utilizing only audio-based speaker recognition
technology. Some embodiments may average the scores of the two SR
models, advantageously providing improved EER (e.g., indicating
that the microphone and accelerometer sensors may be highly
complementary to the task of speaker identification). In noisy
environments, the accelerometer-based EER may be consistently lower
than the microphone-based EER. Accordingly, some embodiments may
utilize only the accelerometer-based score in noisy environments,
or may utilize a weighted average based on the level of background
noise.
[0033] Turning now to FIG. 7, an embodiment of a speaker
recognition process flow 70 may include providing a microphone
signal 71 to a microphone speaker ID classifier 72 in parallel with
providing an accelerometer signal 73 to an accelerometer speaker ID
classifier 74. The microphone signal 71 may also be provided to a
SNR estimation module 75. In some embodiments, the accelerometer
signal 73 may also be provided to the SNR estimation module 75 to
help discriminate which moments of the microphone signal 71 may
correspond to the user's voice and which may correspond just to
background noise. An appropriate weight may be applied to the score
of the microphone speaker ID classifier 72 at block 76 based on the
SNR estimation 75, and a respective weight may also be applied to
the score of the accelerometer speaker ID classifier 74 at block 77
based on the SNR estimation 75. The weighted scores may be combined
(e.g., a weighted average may be determined) to provide a score
fusion 78. For example, in a low noise environment the two scores
may be equally weighted while in a noisy environment the score of
the microphone speaker ID classifier 72 may receive no weight such
that only the score of the accelerometer speaker ID classifier 74
may be used for the score fusion 78.
[0034] Turning now to FIG. 8, an embodiment of a speaker
recognition process flow 80 may include providing accelerometer
data 81, microphone data 82, and SNR data 83 to a multi-modal
speaker ID classifier 84 to produce a speaker ID score 85. With
suitable training, the multi-modal speaker ID classifier 84 may
produce accurate results for a wide variety of noise environments.
Some embodiments may advantageously provide lower EER in all
conditions, may be robust to resisting replay attacks, and/or may
provide speaker recognition in various wearable devices form
factors (e.g., including glasses).
[0035] FIG. 9 illustrates an embodiment of a system 700. In
embodiments, system 700 may be a media system although system 700
is not limited to this context. For example, system 700 may be
incorporated into a personal computer (PC), laptop computer,
ultra-laptop computer, tablet, touch pad, portable computer,
handheld computer, palmtop computer, personal digital assistant
(PDA), cellular telephone, combination cellular telephone/PDA,
television, smart device (e.g., smart phone, smart tablet or smart
television), mobile internet device (MID), messaging device, data
communication device, and so forth.
[0036] In embodiments, the system 700 comprises a platform 702
coupled to a display 720 that presents visual content. The platform
702 may receive video bitstream content from a content device such
as content services device(s) 730 or content delivery device(s) 740
or other similar content sources. A navigation controller 750
comprising one or more navigation features may be used to interact
with, for example, platform 702 and/or display 720. Each of these
components is described in more detail below.
[0037] In embodiments, the platform 702 may comprise any
combination of a chipset 705, processor 710, memory 712, storage
714, graphics subsystem 715, applications 716 and/or radio 718
(e.g., network controller). The chipset 705 may provide
intercommunication among the processor 710, memory 712, storage
714, graphics subsystem 715, applications 716 and/or radio 718. For
example, the chipset 705 may include a storage adapter (not
depicted) capable of providing intercommunication with the storage
714.
[0038] The processor 710 may be implemented as Complex Instruction
Set Computer (CISC) or Reduced Instruction Set Computer (RISC)
processors, x86 instruction set compatible processors, multi-core,
or any other microprocessor or central processing unit (CPU). In
embodiments, the processor 710 may comprise dual-core processor(s),
dual-core mobile processor(s), and so forth.
[0039] The memory 712 may be implemented as a volatile memory
device such as, but not limited to, a Random Access Memory (RAM),
Dynamic Random Access Memory (DRAM), or Static RAM (SRAM).
[0040] The storage 714 may be implemented as a non-volatile storage
device such as, but not limited to, a magnetic disk drive, optical
disk drive, tape drive, an internal storage device, an attached
storage device, flash memory, battery backed-up SDRAM (synchronous
DRAM), and/or a network accessible storage device. In embodiments,
storage 714 may comprise technology to increase the storage
performance enhanced protection for valuable digital media when
multiple hard drives are included, for example.
[0041] The graphics subsystem 715 may perform processing of images
such as still or video for display. The graphics subsystem 715 may
be a graphics processing unit (GPU) or a visual processing unit
(VPU), for example. An analog or digital interface may be used to
communicatively couple the graphics subsystem 715 and display 720.
For example, the interface may be any of a High-Definition
Multimedia Interface (HDMI), DisplayPort, wireless HDMI, and/or
wireless HD compliant techniques. The graphics subsystem 715 could
be integrated into processor 710 or chipset 705. The graphics
subsystem 715 could be a stand-alone card communicatively coupled
to the chipset 705. In one example, the graphics subsystem 715
includes a noise reduction subsystem as described herein.
[0042] The graphics and/or video processing techniques described
herein may be implemented in various hardware architectures. For
example, graphics and/or video functionality may be integrated
within a chipset. Alternatively, a discrete graphics and/or video
processor may be used. As still another embodiment, the graphics
and/or video functions may be implemented by a general purpose
processor, including a multi-core processor. In a further
embodiment, the functions may be implemented in a consumer
electronics device.
[0043] The radio 718 may be a network controller including one or
more radios capable of transmitting and receiving signals using
various suitable wireless communications techniques. Such
techniques may involve communications across one or more wireless
networks. Exemplary wireless networks include (but are not limited
to) wireless local area networks (WLANs), wireless personal area
networks (WPANs), wireless metropolitan area network (WMANs),
cellular networks, and satellite networks. In communicating across
such networks, radio 718 may operate in accordance with one or more
applicable standards in any version.
[0044] In embodiments, the display 720 may comprise any television
type monitor or display. The display 720 may comprise, for example,
a computer display screen, touch screen display, video monitor,
television-like device, and/or a television. The display 720 may be
digital and/or analog. In embodiments, the display 720 may be a
holographic display. Also, the display 720 may be a transparent
surface that may receive a visual projection. Such projections may
convey various forms of information, images, and/or objects. For
example, such projections may be a visual overlay for a mobile
augmented reality (MAR) application. Under the control of one or
more software applications 716, the platform 702 may display user
interface 722 on the display 720.
[0045] In embodiments, content services device(s) 730 may be hosted
by any national, international and/or independent service and thus
accessible to the platform 702 via the Internet, for example. The
content services device(s) 730 may be coupled to the platform 702
and/or to the display 720. The platform 702 and/or content services
device(s) 730 may be coupled to a network 760 to communicate (e.g.,
send and/or receive) media information to and from network 760. The
content delivery device(s) 740 also may be coupled to the platform
702 and/or to the display 720.
[0046] In embodiments, the content services device(s) 730 may
comprise a cable television box, personal computer, network,
telephone, Internet enabled devices or appliance capable of
delivering digital information and/or content, and any other
similar device capable of unidirectionally or bidirectionally
communicating content between content providers and platform 702
and/display 720, via network 760 or directly. It will be
appreciated that the content may be communicated unidirectionally
and/or bidirectionally to and from any one of the components in
system 700 and a content provider via network 760. Examples of
content may include any media information including, for example,
video, music, medical and gaming information, and so forth.
[0047] The content services device(s) 730 receives content such as
cable television programming including media information, digital
information, and/or other content. Examples of content providers
may include any cable or satellite television or radio or Internet
content providers. The provided examples are not meant to limit
embodiments.
[0048] In embodiments, the platform 702 may receive control signals
from a navigation controller 750 having one or more navigation
features. The navigation features of the controller 750 may be used
to interact with the user interface 722, for example. In
embodiments, the navigation controller 750 may be a pointing device
that may be a computer hardware component (specifically human
interface device) that allows a user to input spatial (e.g.,
continuous and multi-dimensional) data into a computer. Many
systems such as graphical user interfaces (GUI), and televisions
and monitors allow the user to control and provide data to the
computer or television using physical gestures.
[0049] Movements of the navigation features of the controller 750
may be echoed on a display (e.g., display 720) by movements of a
pointer, cursor, focus ring, or other visual indicators displayed
on the display. For example, under the control of software
applications 716, the navigation features located on the navigation
controller 750 may be mapped to virtual navigation features
displayed on the user interface 722, for example. In embodiments,
the controller 750 may not be a separate component but integrated
into the platform 702 and/or the display 720. Embodiments, however,
are not limited to the elements or in the context shown or
described herein.
[0050] In embodiments, drivers (not shown) may comprise technology
to enable users to instantly turn on and off the platform 702 like
a television with the touch of a button after initial boot-up, when
enabled, for example. Program logic may allow the platform 702 to
stream content to media adaptors or other content services
device(s) 730 or content delivery device(s) 740 when the platform
is turned "off." In addition, chipset 705 may comprise hardware
and/or software support for 5.1 surround sound audio and/or high
definition 7.1 surround sound audio, for example. Drivers may
include a graphics driver for integrated graphics platforms. In
embodiments, the graphics driver may comprise a peripheral
component interconnect (PCI) Express graphics card.
[0051] In various embodiments, any one or more of the components
shown in the system 700 may be integrated. For example, the
platform 702 and the content services device(s) 730 may be
integrated, or the platform 702 and the content delivery device(s)
740 may be integrated, or the platform 702, the content services
device(s) 730, and the content delivery device(s) 740 may be
integrated, for example. In various embodiments, the platform 702
and the display 720 may be an integrated unit. The display 720 and
content service device(s) 730 may be integrated, or the display 720
and the content delivery device(s) 740 may be integrated, for
example. These examples are not meant to limit the embodiments.
[0052] In various embodiments, system 700 may be implemented as a
wireless system, a wired system, or a combination of both. When
implemented as a wireless system, system 700 may include components
and interfaces suitable for communicating over a wireless shared
media, such as one or more antennas, transmitters, receivers,
transceivers, amplifiers, filters, control logic, and so forth. An
example of wireless shared media may include portions of a wireless
spectrum, such as the RF spectrum and so forth. When implemented as
a wired system, system 700 may include components and interfaces
suitable for communicating over wired communications media, such as
input/output (I/O) adapters, physical connectors to connect the I/O
adapter with a corresponding wired communications medium, a network
interface card (NIC), disc controller, video controller, audio
controller, and so forth. Examples of wired communications media
may include a wire, cable, metal leads, printed circuit board
(PCB), backplane, switch fabric, semiconductor material,
twisted-pair wire, co-axial cable, fiber optics, and so forth.
[0053] The platform 702 may establish one or more logical or
physical channels to communicate information. The information may
include media information and control information. Media
information may refer to any data representing content meant for a
user. Examples of content may include, for example, data from a
voice conversation, videoconference, streaming video, electronic
mail ("email") message, voice mail message, alphanumeric symbols,
graphics, image, video, text and so forth. Data from a voice
conversation may be, for example, speech information, silence
periods, background noise, comfort noise, tones and so forth.
Control information may refer to any data representing commands,
instructions or control words meant for an automated system. For
example, control information may be used to route media information
through a system, or instruct a node to process the media
information in a predetermined manner. The embodiments, however,
are not limited to the elements or in the context shown or
described in FIG. 9.
[0054] As described above, the system 700 may be embodied in
varying physical styles or form factors. FIG. 10 illustrates
embodiments of a small form factor device 800 in which the system
700 may be embodied. In embodiments, for example, the device 800
may be implemented as a mobile computing device having wireless
capabilities. A mobile computing device may refer to any device
having a processing system and a mobile power source or supply,
such as one or more batteries, for example.
[0055] As described above, examples of a mobile computing device
may include a personal computer (PC), laptop computer, ultra-laptop
computer, tablet, touch pad, portable computer, handheld computer,
palmtop computer, personal digital assistant (PDA), cellular
telephone, combination cellular telephone/PDA, television, smart
device (e.g., smart phone, smart tablet or smart television),
mobile internet device (MID), messaging device, data communication
device, and so forth.
[0056] Examples of a mobile computing device also may include
computers that are arranged to be worn by a person, such as a wrist
computer, finger computer, ring computer, eyeglass computer,
belt-clip computer, arm-band computer, shoe computers, clothing
computers, and other wearable computers. In embodiments, for
example, a mobile computing device may be implemented as a smart
phone capable of executing computer applications, as well as voice
communications and/or data communications. Although some
embodiments may be described with a mobile computing device
implemented as a smart phone by way of example, it may be
appreciated that other embodiments may be implemented using other
wireless mobile computing devices as well. The embodiments are not
limited in this context.
[0057] As shown in FIG. 10, the device 800 may comprise a housing
802, a display 804, an input/output (I/O) device 806, and an
antenna 808. The device 800 also may comprise navigation features
812. The display 804 may comprise any suitable display unit for
displaying information appropriate for a mobile computing device.
The I/O device 806 may comprise any suitable I/O device for
entering information into a mobile computing device. Examples for
the I/O device 806 may include an alphanumeric keyboard, a numeric
keypad, a touch pad, input keys, buttons, switches, rocker
switches, microphones, speakers, voice recognition device and
software, and so forth. Information also may be entered into the
device 800 by way of microphone. Such information may be digitized
by a voice recognition device. The embodiments are not limited in
this context.
[0058] Some embodiments of the system 700 and/or the device 800 may
include aspects or features of the embodiments described herein,
including one or more aspects of the following Examples.
ADDITIONAL NOTES AND EXAMPLES
[0059] Example 1 may include an electronic processing system,
comprising a processor, memory communicatively coupled to the
processor, and logic communicatively coupled to the processor to
acquire vibration information corresponding to a speaker, and
identify the speaker based on the vibration information.
[0060] Example 2 may include the system of Example 1, wherein the
logic is further to acquire audio information corresponding to the
speaker, and identify the speaker based on both the audio
information and the vibration information.
[0061] Example 3 may include the system of Example 2, wherein the
logic is further to perform speaker recognition based on the audio
information to determine a first recognition score, perform speaker
recognition based on the vibration information to determine a
second recognition score, and identify the speaker based on an
average of the first recognition score and the second recognition
score.
[0062] Example 4 may include the system of Example 3, wherein the
logic is further to determine a level of background noise, compare
the level of background noise against a threshold, identify the
speaker based on both the audio information and the vibration
information if the level of background noise is less than the
threshold, and identify the speaker based on only the vibration
information if the level of background noise is greater than or
equal to the threshold.
[0063] Example 5 may include the system of Example 3, wherein the
logic is further to determine a level of background noise, apply
weights to the first and second recognition scores based on the
level of background noise, and identify the speaker based on an
average of the weighted first recognition score and the weighted
second recognition score.
[0064] Example 6 may include the system of any of Examples 2 to 5,
wherein the logic is further to acquire the audio information from
a microphone, and acquire vibration information from an
accelerometer.
[0065] Example 7 may include a semiconductor package apparatus,
comprising one or more substrates, and logic coupled to the one or
more substrates, wherein the logic is at least partly implemented
in one or more of configurable logic and fixed-functionality
hardware logic, the logic coupled to the one or more substrates to
acquire vibration information corresponding to a speaker, and
identify the speaker based on the vibration information.
[0066] Example 8 may include the apparatus of Example 7, wherein
the logic is further to acquire audio information corresponding to
the speaker, and identify the speaker based on both the audio
information and the vibration information.
[0067] Example 9 may include the apparatus of Example 8, wherein
the logic is further to perform speaker recognition based on the
audio information to determine a first recognition score, perform
speaker recognition based on the vibration information to determine
a second recognition score, and identify the speaker based on an
average of the first recognition score and the second recognition
score.
[0068] Example 10 may include the apparatus of Example 9, wherein
the logic is further to determine a level of background noise,
compare the level of background noise against a threshold, identify
the speaker based on both the audio information and the vibration
information if the level of background noise is less than the
threshold, and identify the speaker based on only the vibration
information if the level of background noise is greater than or
equal to the threshold.
[0069] Example 11 may include the apparatus of Example 9, wherein
the logic is further to determine a level of background noise,
apply weights to the first and second recognition scores based on
the level of background noise, and identify the speaker based on an
average of the weighted first recognition score and the weighted
second recognition score.
[0070] Example 12 may include the apparatus of any of Examples 8 to
11, wherein the logic is further to acquire the audio information
from a microphone, and acquire vibration information from an
accelerometer.
[0071] Example 13 may include the semiconductor package apparatus
of Example 7, wherein the logic coupled to the one or more
substrates includes transistor channel regions that are positioned
within the one or more substrates.
[0072] Example 14 may include a method of identifying a speaker,
comprising acquiring vibration information corresponding to a
speaker, and identifying the speaker based on the vibration
information.
[0073] Example 15 may include the method of Example 14, further
comprising acquiring audio information corresponding to the
speaker, identifying the speaker based on both the audio
information and the vibration information.
[0074] Example 16 may include the method of Example 15, further
comprising performing speaker recognition based on the audio
information to determine a first recognition score, performing
speaker recognition based on the vibration information to determine
a second recognition score, and identifying the speaker based on an
average of the first recognition score and the second recognition
score.
[0075] Example 17 may include the method of Example 16, further
comprising determining a level of background noise, comparing the
level of background noise against a threshold, identifying the
speaker based on both the audio information and the vibration
information if the level of background noise is less than the
threshold, and identifying the speaker based on only the vibration
information if the level of background noise is greater than or
equal to the threshold.
[0076] Example 18 may include the method of Example 16, further
comprising determining a level of background noise, applying
weights to the first and second recognition scores based on the
level of background noise, and identifying the speaker based on an
average of the weighted first recognition score and the weighted
second recognition score.
[0077] Example 19 may include the method of any of Examples 15 to
18, further comprising acquiring the audio information from a
microphone, and acquiring vibration information from an
accelerometer.
[0078] Example 20 may include at least one computer readable
medium, comprising a set of instructions, which when executed by a
computing device, can cause the computing device to acquire
vibration information corresponding to a speaker, and identify the
speaker based on the vibration information.
[0079] Example 21 may include the at least one computer readable
medium of Example 20, comprising a further set of instructions,
which when executed by the computing device, can cause the
computing device to acquire audio information corresponding to the
speaker, and identify the speaker based on both the audio
information and the vibration information.
[0080] Example 22 may include the at least one computer readable
medium of Example 21, comprising a further set of instructions,
which when executed by the computing device, cause the computing
device to perform speaker recognition based on the audio
information to determine a first recognition score, perform speaker
recognition based on the vibration information to determine a
second recognition score, and identify the speaker based on an
average of the first recognition score and the second recognition
score.
[0081] Example 23 may include the at least one computer readable
medium of Example 22, comprising a further set of instructions,
which when executed by the computing device, cause the computing
device to determine a level of background noise, compare the level
of background noise against a threshold, identify the speaker based
on both the audio information and the vibration information if the
level of background noise is less than the threshold, and identify
the speaker based on only the vibration information if the level of
background noise is greater than or equal to the threshold.
[0082] Example 24 may include the at least one computer readable
medium of Example 22, comprising a further set of instructions,
which when executed by the computing device, cause the computing
device to determine a level of background noise, apply weights to
the first and second recognition scores based on the level of
background noise, and identify the speaker based on an average of
the weighted first recognition score and the weighted second
recognition score.
[0083] Example 25 may include the at least one computer readable
medium of any of Examples 21 to 24, comprising a further set of
instructions, which when executed by the computing device, cause
the computing device to acquire the audio information from a
microphone, and acquire vibration information from an
accelerometer.
[0084] Example 26 may include a speaker recognition apparatus,
comprising means for acquiring vibration information corresponding
to a speaker, and means for identifying the speaker based on the
vibration information.
[0085] Example 27 may include the apparatus of Example 26, further
comprising means for acquiring audio information corresponding to
the speaker, means for identifying the speaker based on both the
audio information and the vibration information.
[0086] Example 28 may include the apparatus of Example 27, further
comprising means for performing speaker recognition based on the
audio information to determine a first recognition score, means for
performing speaker recognition based on the vibration information
to determine a second recognition score, and means for identifying
the speaker based on an average of the first recognition score and
the second recognition score.
[0087] Example 29 may include the apparatus of Example 28, further
comprising means for determining a level of background noise, means
for comparing the level of background noise against a threshold,
means for identifying the speaker based on both the audio
information and the vibration information if the level of
background noise is less than the threshold, and means for
identifying the speaker based on only the vibration information if
the level of background noise is greater than or equal to the
threshold.
[0088] Example 30 may include the apparatus of Example 28, further
comprising means for determining a level of background noise, means
for applying weights to the first and second recognition scores
based on the level of background noise, and means for identifying
the speaker based on an average of the weighted first recognition
score and the weighted second recognition score.
[0089] Example 31 may include the apparatus of any of Examples 27
to 30, further comprising means for acquiring the audio information
from a microphone, and means for acquiring vibration information
from an accelerometer.
[0090] Example 32 may include a wearable apparatus, comprising a
wearable housing to be worn by a user, an accelerometer supported
by the wearable housing, and a microphone supported by the wearable
housing.
[0091] Example 33 may include the apparatus of Example 32, further
comprising a communication module to communicate information
related to one or more of the accelerometer and the microphone to a
speaker recognition module.
[0092] Example 34 may include the apparatus of Example 33, wherein
the communication module comprises a radio and an antenna for
wireless communication.
[0093] Example 35 may include the apparatus of any of Examples 32
to 34, wherein the wearable housing comprises a form factor of
eyeglasses, and wherein the accelerometer is positioned in one or
more of a bridge of the eyeglasses and a nose piece of the
eyeglasses.
[0094] Embodiments are applicable for use with all types of
semiconductor integrated circuit ("IC") chips. Examples of these IC
chips include but are not limited to processors, controllers,
chipset components, programmable logic arrays (PLAs), memory chips,
network chips, systems on chip (SoCs), SSD/NAND controller ASICs,
and the like. In addition, in some of the drawings, signal
conductor lines are represented with lines. Some may be different,
to indicate more constituent signal paths, have a number label, to
indicate a number of constituent signal paths, and/or have arrows
at one or more ends, to indicate primary information flow
direction. This, however, should not be construed in a limiting
manner. Rather, such added detail may be used in connection with
one or more exemplary embodiments to facilitate easier
understanding of a circuit. Any represented signal lines, whether
or not having additional information, may actually comprise one or
more signals that may travel in multiple directions and may be
implemented with any suitable type of signal scheme, e.g., digital
or analog lines implemented with differential pairs, optical fiber
lines, and/or single-ended lines.
[0095] Example sizes/models/values/ranges may have been given,
although embodiments are not limited to the same. As manufacturing
techniques (e.g., photolithography) mature over time, it is
expected that devices of smaller size could be manufactured. In
addition, well known power/ground connections to IC chips and other
components may or may not be shown within the figures, for
simplicity of illustration and discussion, and so as not to obscure
certain aspects of the embodiments. Further, arrangements may be
shown in block diagram form in order to avoid obscuring
embodiments, and also in view of the fact that specifics with
respect to implementation of such block diagram arrangements are
highly dependent upon the platform within which the embodiment is
to be implemented, i.e., such specifics should be well within
purview of one skilled in the art. Where specific details (e.g.,
circuits) are set forth in order to describe example embodiments,
it should be apparent to one skilled in the art that embodiments
can be practiced without, or with variation of, these specific
details. The description is thus to be regarded as illustrative
instead of limiting.
[0096] The term "coupled" may be used herein to refer to any type
of relationship, direct or indirect, between the components in
question, and may apply to electrical, mechanical, fluid, optical,
electromagnetic, electromechanical or other connections. In
addition, the terms "first", "second", etc. may be used herein only
to facilitate discussion, and carry no particular temporal or
chronological significance unless otherwise indicated.
[0097] As used in this application and in the claims, a list of
items joined by the term "one or more of" may mean any combination
of the listed terms. For example, the phrase "one or more of A, B,
and C" and the phrase "one or more of A, B, or C" both may mean A;
B; C; A and B; A and C; B and C; or A, B and C.
[0098] Those skilled in the art will appreciate from the foregoing
description that the broad techniques of the embodiments can be
implemented in a variety of forms. Therefore, while the embodiments
have been described in connection with particular examples thereof,
the true scope of the embodiments should not be so limited since
other modifications will become apparent to the skilled
practitioner upon a study of the drawings, specification, and
following claims.
* * * * *